In manufacturing and assembly processes, it is often desirable to analyze an object surface to determine the nature of features and/or irregularities. The displacement (or “profile”) of the object surface can be determined using a machine vision system (also termed herein “vision system”) in the form of a laser displacement sensor (also termed a laser beam “profiler”). A laser displacement sensor uses a planar curtain or “fan” of a laser beam (also termed herein a “laser plane”) to capture and determine the two-dimensional profile of a scanned object surface within the laser plane and transverse to the beam propagation path. Multiple two-dimensional profiles may be assembled to create a three-dimensional (3D) representation. In a conventional arrangement, a vision system camera assembly is oriented to view the plane of the beam from outside the plane. This arrangement captures the profile of the projected line (e.g. extending along the physical x-axis) on the object surface, which, due to the baseline (i.e. the relative spacing along the y-axis) between the beam (sometimes characterized as a “fan”) plane and the camera causes the imaged line to appear as varying in the image y-axis direction as a function of the physical z-axis height of the imaged point (along the image x axis). This deviation represents the profile of the surface. Laser displacement sensors are useful in a wide range of inspection and manufacturing operations where the user desires to measure and characterize surface details of a scanned object via triangulation. One form of laser displacement sensor uses a vision system camera having a lens assembly and image sensor (or “imager”) that can be based upon a CCD or CMOS design. The imager defines a predetermined field of grayscale or color-sensing pixels on an image plane that receives focused light from an imaged scene through a lens.
In a typical arrangement, the displacement sensor(s) and/or object are in relative motion (usually in the physical y-coordinate direction) so that the object surface is scanned by the sensor(s), and a sequence of images are acquired of the laser line at desired spatial intervals—typically in association with an encoder or other motion measurement device (or, alternatively, at time based intervals). Each of these single profile lines is typically derived from a single acquired image. These lines collectively describe the surface of the imaged object and surrounding imaged scene and define a “range image” or “depth image”.
Other camera assemblies can also be employed to capture a 3D image (range image) of an object in a scene. The term range image is used to characterize an image (a two-dimensional array of values) with pel values characterizing Z height at each location, or characterizing that no height is available at that location. The term range image is alternatively used to refer to generic 3D data, such as 3D point cloud data, or 3D mesh data. The term range and gray image is used to characterize an image with pel values characterizing both z-height and associated gray level at each location, or characterizing that no height is available at that location, or alternatively a range and gray image can be characterized by two corresponding images—one image characterizing z-height at each location, or characterizing that no z-height is available at that location. For example, structured light systems, stereo vision systems, DLP metrology, and other arrangements can be employed. These systems all generate an image that provides a height value (e.g. z-coordinate) to pixels.
A 3D range image generated by various types of camera assemblies (or combinations thereof) can be used to locate and determine the presence and/or characteristics of particular features on the object surface. In certain vision system implementations, such as the inspection of circuit boards, a plurality of displacement sensors (e.g. laser profilers) are mounted together to extend the overall field of view (FOV) (wherein the term “field of view” refers to measurement range) of the vision system so as to fully image a desired area of the object (e.g. its full width) with sufficient resolution. In the example of a laser profiler, the object moves in relative motion with respect to the camera(s) so as to provide a scanning function that allows construction of a range (or, more generally a “3D”) image from a sequence of slices acquired at various motion positions. This is often implemented using a conveyor, motion stage, robot end effector or other motion conveyance. This motion can be the basis of a common (motion) coordinate space with the y-axis defined along the direction of “scan” motion.
3D sensors can be used to acquire images of surfaces that contain various types of symbology codes, also termed simply “IDs”. IDs are arranged to that geometric shapes therein contain various data (e.g. alphanumeric data) that conveys information to the reader about the underlying object or other subject matter (e.g. an address, a website URL, etc.). In many applications, such IDs are used to identify, and allow tracking of, objects passing through a production or logistics operation—often on a moving conveyor that transports the objects between various handling/manufacturing stations. Such IDs can constitute (e.g.) so-called one-dimensional (1D) barcodes or so-called two-dimensional (2D) codes, including (e.g.) QR codes, DotCode, etc.). IDs can be applied to a surface by direct printing, adhesive labels, peening and/or surface formation—such as molding the code into the surface of the object, thereby defining 3D surface features with the elements of the ID. As defined herein IDs can also be considered strings of alphanumeric characters and/or graphics applied to an object surface. Such characters/graphics can be detected, identified, read, or decoded using appropriate processes and/or processors including optical character recognition (OCR) reading hardware and/or software routines.
At various locations along a conveyor line transporting ID-containing objects, an ID reading arrangement (an “ID reader”) and associated process(ors)(es) and/or software can be provided to image objects passing thereunder. One or more 3D sensors can be employed to scan the surface of an object moving along the conveyor through the ID reader arrangement. However, IDs on objects having certain shapes, sizes and/or surface textures can prove highly challenging to read and decode accurately, particularly at a reasonable throughput speed. For example, it is often challenging to decode IDs molded into the surface of curved and/or sloped objects, such as annular/toroidal vehicle tires. Tires are relatively large and the ID can be located in a small discrete area on the overall surface. The surface can include a variety of textures, lettering and other 3D surface patterns that increase the challenge of finding and decoding an ID. Because tires are fairly large in radius, compared to the field of view (FOV) needed to provide a sufficiently high-resolution scan of the surface to resolve ID features, a plurality of 3D scanners are needed to provide a sufficient scan width. In addition, the side of a tire is often sloped increasing the difficulty of resolving ID features.